The standout reason why Python is quite popular is the large endowment of libraries. Each library is unique, yet extensive enough to enable programmers to solve many data problems every day. The following are some of the top libraries used in data science:
● NumPy
For numerical computations, you need Numerical Python (NumPy). NumPy is considered the foundation of numerical computations in Python. It is a general purpose array processor that uses N-dimensional array objects.
NumPy is an efficient library given that when using multidimensional arrays, you have operators and functions that work with multidimensional arrays, thereby eliminating the slowness challenge during numerical computations.
NumPy functions are precompiled, helping you complete numerical routines faster than other libraries. Through NumPy’s approach, you can perform computations faster and efficiently, especially when using vectors. NumPy is a mainstay in data analysis when you need powerful N-dimensional arrays. Libraries like Scikit-learn and SciPy have NumPy as their foundation, and you can also use NumPy in place of MATLAB if you are working with Matplotlib and SciPy.
● TensorFlow
If you are working on a high-performance computation project, TensorFlow is your best bet. There are thousands of contributors working on this library, which is a good resource pool whenever you are struggling with something.
Through TensorFlow, data scientists are able to define and run computations with tensors. A tensor is a computational object that can be manipulated to derive values. In this library, you can expect high-quality graphical visualizations, which makes it easier for you to present projects to an audience.
In neural machine learning, TensorFlow is preferred by developers because it helps them reduce errors in computations by up to 60%. This further allows them to perform parallel computing. Through parallel computing, developers can then build complex projects and execute them in a fairly simple manner.
Another benefit of using the TensorFlow library is that it enjoys support from Google. This partnership comes in handy especially in library management, as the tech giant allows a seamless support framework when using the library.
Besides that, you will always have some of the latest features when using TensorFlow because the development team behind it release updates frequently, and you can install them faster than most libraries.
Given all these benefits, you will find TensorFlow coming in handy when working on video detection projects, time series analysis, text applications, and image or speech recognition projects.
● Matplotlib
For data visualizations, Matplotlib provides some of the most amazing results in data science. It is by far the best plotting library you will use in Python. Matplotlib is essentially a data visualization library, hence the wide range of plots and graphs. To extend its utility further, Matplotlib also comes with an object-oriented API through which you can add the visualizations created into different apps.
If you have been working with MATLAB in the past, Matplotlib is a better alternative. Being an open-source library, usage is free, and you have access to a large pool of experts who can assist you in so many ways. When using Matplotlib, you are not restricted in terms of the operating system.
You can work with lots of output types and backends, thereby allowing you to create visualizations in any format you desire. Perhaps one of the best things about using Matplotlib is its behavior in use. It is very easy on memory consumption compared to other libraries. Because of the efficient memory consumption, you should expect a smooth experience at runtime, too.
Matplotlib visualizations are useful when analyzing the correlation between different variables. It presents each variable in a unique way, making it easier to spot the similarities and differences between them. You can also use it to detect outliers in a scatter plot or identify uniqueness in data distribution, helping you get a better insight into the data you are studying.
● Pandas
Python Data Analysis (Pandas) is another important library that you cannot miss in data science. Together with Matplotlib and NumPy, this library comes in handy, especially for cleaning data. Data structures in Pandas are flexible and efficient, allowing you an intuitive and easy way to program structured data.
Concerning the need to clean or wrangle data, Pandas comes second to none. Many data analysts store data in CSV files and other database files. Pandas has exceptional support especially for CSV files, allowing you to access data frames and perform transformations like extract, transform, and load on the data sets in question.
The Pandas syntax is elaborate with incredible functions to enable you to produce amazing results even if your data set is missing some fragments of data. Through Pandas, you can build unique functions and test them on different sets of data.
Pandas helps data scientists in many commercial, financial, and academic fields, especially when dealing with statistical data analysis. It is also a good library for financial computations and has recently been introduced into neuroscience.
● SciPy
For high-level computations in data science, you need Scientific Python (SciPy). It is an open-source library with thousands of members in the contributor community. SciPy is an extension of NumPy, therefore you can expect the same efficiency in NumPy when you are working on technical and scientific computations. It makes the scientific calculation more user-friendly due to the fact that its functions and algorithms are an extension of NumPy.
You will find SciPy easier to work with if you are ever working on differential problems because its functions are built into the library. This, coupled with the ndimage sub module helps in processing multidimensional images faster. The high speed is another reason why SciPy is a reliable library for data visualization and manipulation.
Where is SciPy applicable? As a data scientist, you will need SciPy if your work involves linear algebra, working with optimization algorithms, Fourier transform or any differential equations, and operations that involve multidimensional images.
These are the main libraries you will use for data. In case you are using pip, you can install the directories through the following commands:
pip install numpy
pip install scipy
pip install matplotlib
pip install ipython
In the coming posts we'll start exploring these libraries starting with NumPy.
● NumPy
For numerical computations, you need Numerical Python (NumPy). NumPy is considered the foundation of numerical computations in Python. It is a general purpose array processor that uses N-dimensional array objects.
NumPy is an efficient library given that when using multidimensional arrays, you have operators and functions that work with multidimensional arrays, thereby eliminating the slowness challenge during numerical computations.
NumPy functions are precompiled, helping you complete numerical routines faster than other libraries. Through NumPy’s approach, you can perform computations faster and efficiently, especially when using vectors. NumPy is a mainstay in data analysis when you need powerful N-dimensional arrays. Libraries like Scikit-learn and SciPy have NumPy as their foundation, and you can also use NumPy in place of MATLAB if you are working with Matplotlib and SciPy.
● TensorFlow
If you are working on a high-performance computation project, TensorFlow is your best bet. There are thousands of contributors working on this library, which is a good resource pool whenever you are struggling with something.
Through TensorFlow, data scientists are able to define and run computations with tensors. A tensor is a computational object that can be manipulated to derive values. In this library, you can expect high-quality graphical visualizations, which makes it easier for you to present projects to an audience.
In neural machine learning, TensorFlow is preferred by developers because it helps them reduce errors in computations by up to 60%. This further allows them to perform parallel computing. Through parallel computing, developers can then build complex projects and execute them in a fairly simple manner.
Another benefit of using the TensorFlow library is that it enjoys support from Google. This partnership comes in handy especially in library management, as the tech giant allows a seamless support framework when using the library.
Besides that, you will always have some of the latest features when using TensorFlow because the development team behind it release updates frequently, and you can install them faster than most libraries.
Given all these benefits, you will find TensorFlow coming in handy when working on video detection projects, time series analysis, text applications, and image or speech recognition projects.
● Matplotlib
For data visualizations, Matplotlib provides some of the most amazing results in data science. It is by far the best plotting library you will use in Python. Matplotlib is essentially a data visualization library, hence the wide range of plots and graphs. To extend its utility further, Matplotlib also comes with an object-oriented API through which you can add the visualizations created into different apps.
If you have been working with MATLAB in the past, Matplotlib is a better alternative. Being an open-source library, usage is free, and you have access to a large pool of experts who can assist you in so many ways. When using Matplotlib, you are not restricted in terms of the operating system.
You can work with lots of output types and backends, thereby allowing you to create visualizations in any format you desire. Perhaps one of the best things about using Matplotlib is its behavior in use. It is very easy on memory consumption compared to other libraries. Because of the efficient memory consumption, you should expect a smooth experience at runtime, too.
Matplotlib visualizations are useful when analyzing the correlation between different variables. It presents each variable in a unique way, making it easier to spot the similarities and differences between them. You can also use it to detect outliers in a scatter plot or identify uniqueness in data distribution, helping you get a better insight into the data you are studying.
● Pandas
Python Data Analysis (Pandas) is another important library that you cannot miss in data science. Together with Matplotlib and NumPy, this library comes in handy, especially for cleaning data. Data structures in Pandas are flexible and efficient, allowing you an intuitive and easy way to program structured data.
Concerning the need to clean or wrangle data, Pandas comes second to none. Many data analysts store data in CSV files and other database files. Pandas has exceptional support especially for CSV files, allowing you to access data frames and perform transformations like extract, transform, and load on the data sets in question.
The Pandas syntax is elaborate with incredible functions to enable you to produce amazing results even if your data set is missing some fragments of data. Through Pandas, you can build unique functions and test them on different sets of data.
Pandas helps data scientists in many commercial, financial, and academic fields, especially when dealing with statistical data analysis. It is also a good library for financial computations and has recently been introduced into neuroscience.
● SciPy
For high-level computations in data science, you need Scientific Python (SciPy). It is an open-source library with thousands of members in the contributor community. SciPy is an extension of NumPy, therefore you can expect the same efficiency in NumPy when you are working on technical and scientific computations. It makes the scientific calculation more user-friendly due to the fact that its functions and algorithms are an extension of NumPy.
You will find SciPy easier to work with if you are ever working on differential problems because its functions are built into the library. This, coupled with the ndimage sub module helps in processing multidimensional images faster. The high speed is another reason why SciPy is a reliable library for data visualization and manipulation.
Where is SciPy applicable? As a data scientist, you will need SciPy if your work involves linear algebra, working with optimization algorithms, Fourier transform or any differential equations, and operations that involve multidimensional images.
These are the main libraries you will use for data. In case you are using pip, you can install the directories through the following commands:
pip install numpy
pip install scipy
pip install matplotlib
pip install ipython
In the coming posts we'll start exploring these libraries starting with NumPy.
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